
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "48"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 46 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 44 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459861	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	3.109155	3.716508	2.833104	2.473712	0.585942	0.217655	-3.900575	-4.402133	0.6868	0.6636	0.4294	nan	nan
2459860	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	4.590615	5.097160	22.435510	22.125633	10.323019	14.678188	-2.299890	-3.351786	0.6866	0.6562	0.4272	nan	nan
2459859	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	3.061670	3.130500	2.915110	2.464707	0.835001	0.216629	-2.412524	-2.922979	0.6933	0.6610	0.4226	nan	nan
2459858	not_connected	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.042627	3.468368	2.634102	2.294387	0.585636	-0.285007	-4.229243	-4.508992	0.7050	0.6687	0.4347	2.834957	2.638903
2459857	not_connected	100.00%	100.00%	100.00%	0.00%	-	-	25.011257	24.319911	11.192288	11.234026	11.908282	12.644056	47.511844	49.794054	0.0329	0.0333	0.0001	nan	nan
2459856	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.456241	6.571342	20.579751	20.140595	4.427210	7.416480	-3.855797	-4.192125	0.6929	0.6816	0.4195	2.539507	2.367029
2459855	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	75.881435	75.479274	inf	inf	4363.644384	4363.637796	4165.170593	4164.518655	0.0078	0.0074	0.0008	0.000000	0.000000
2459854	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.091192	2.905401	12.795624	13.774311	-0.109505	0.860952	-1.663388	-2.396952	0.7075	0.7389	0.4495	2.958963	2.546763
2459853	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.573063	2.390249	16.518311	17.670874	1.761878	2.120566	-1.943922	-3.095155	0.7241	0.6789	0.4453	3.467913	3.190201
2459852	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.751524	4.600523	16.019201	17.930810	7.600349	7.748574	10.669393	12.286929	0.8068	0.8115	0.2810	5.061433	5.313942
2459851	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.424535	5.721825	18.290166	18.712407	5.136981	19.531101	2.595111	12.554404	0.7383	0.7332	0.3645	2.947240	2.757466
2459850	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.756966	2.875865	15.622652	16.339374	2.289820	5.817607	-0.162938	5.688537	0.7264	0.7462	0.3676	3.055441	2.934204
2459849	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.462089	3.252888	31.426852	32.725173	1.455625	1.917992	-1.101786	-1.991814	0.7257	0.7377	0.3724	2.666661	2.601147
2459848	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.249655	3.252682	22.563517	23.646458	2.939821	5.694635	-1.884104	-2.397765	0.7063	0.7439	0.3900	3.020885	2.916021
2459847	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.259168	2.967894	21.219578	22.555682	3.552427	9.952180	-1.091694	-2.153773	0.7107	0.6777	0.4466	2.294399	2.126464
2459846	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.323195	10.686406	16.743820	18.225408	10.055065	9.471941	-0.553845	-1.246931	0.8334	0.6628	0.5100	5.889580	2.806949
2459845	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.599176	3.884000	28.685894	30.209084	2.852849	3.243865	-1.361721	-2.842054	0.7304	0.7494	0.3811	0.000000	0.000000
2459844	not_connected	100.00%	100.00%	100.00%	0.00%	-	-	30.002485	31.338418	77.490359	77.675570	9.441747	10.310696	26.926853	30.985971	0.0329	0.0312	0.0011	nan	nan
2459843	not_connected	100.00%	0.66%	0.66%	0.00%	100.00%	0.00%	8.571797	9.063615	18.894662	18.882324	9.044108	13.570397	-3.467713	-3.625519	0.7234	0.7304	0.4032	3.199406	2.977271
2459842	not_connected	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.692769	1.065845	0.032863	-0.545466	-1.724607	-1.924796	-2.168986	-2.150311	0.7231	0.6295	0.2847	2.815679	2.653751
2459841	not_connected	100.00%	100.00%	100.00%	0.00%	-	-	63.247197	60.254380	72.102045	74.242901	58.350812	61.713181	43.956170	44.148228	0.0325	0.0318	0.0002	nan	nan

Antenna Season Report Notebook¶

Antenna 48 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 48, Node N06:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 48: 2459861

Antenna 48: 2459860

Antenna 48: 2459859

Antenna 48: 2459858

Antenna 48: 2459857

Antenna 48: 2459856

Antenna 48: 2459855

Antenna 48: 2459854

Antenna 48: 2459853

Antenna 48: 2459852

Antenna 48: 2459851

Antenna 48: 2459850

Antenna 48: 2459849

Antenna 48: 2459848

Antenna 48: 2459847

Antenna 48: 2459846

Antenna 48: 2459845

Antenna 48: 2459844

Antenna 48: 2459843

Antenna 48: 2459842

Antenna 48: 2459841

Antenna Season Report Notebook¶

Antenna 48 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 48, Node N06:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 48: 2459861

Antenna 48: 2459860

Antenna 48: 2459859

Antenna 48: 2459858

Antenna 48: 2459857

Antenna 48: 2459856

Antenna 48: 2459855

Antenna 48: 2459854

Antenna 48: 2459853

Antenna 48: 2459852

Antenna 48: 2459851

Antenna 48: 2459850

Antenna 48: 2459849

Antenna 48: 2459848

Antenna 48: 2459847

Antenna 48: 2459846

Antenna 48: 2459845

Antenna 48: 2459844

Antenna 48: 2459843

Antenna 48: 2459842

Antenna 48: 2459841

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶